RESUMO
Recirculating aquaculture systems (RAS) pose unique challenges in microbial community management since they rely on a stable community with key target groups, both in the RAS environment and in the host (in this case, Solea senegalensis). Our goal was to determine how much of the sole microbiome is inherited from the egg stage, and how much is acquired during the remainder of the sole life cycle in an aquaculture production batch, especially regarding potentially probiotic and pathogenic groups. Our work comprises sole tissue samples from 2 days before hatching and up to 146 days after hatching (-2 to 146 DAH), encompassing the egg, larval, weaning, and pre-ongrowing stages. Total DNA was isolated from the different sole tissues, as well as from live feed introduced in the first stages, and 16S rRNA gene was sequenced (V6-V8 region) using the Illumina MiSeq platform. The output was analysed with the DADA2 pipeline, and taxonomic attribution with SILVAngs version 138.1. Using the Bray-Curtis dissimilarity index, both age and life cycle stage appeared to be drivers of bacterial community dissimilarity. To try to distinguish the inherited (present since the egg stage) from the acquired community (detected at later stages), different tissues were analysed at 49, 119 and 146 DAH (gill, intestine, fin and mucus). Only a few genera were inherited, but those that were inherited accompany the sole microbiome throughout the life cycle. Two genera of potentially probiotic bacteria (Bacillus and Enterococcus) were already present in the eggs, while others were acquired later, in particularly, forty days after live feed was introduced. The potentially pathogenic genera Tenacibaculum and Vibrio were inherited from the eggs, while Photobacterium and Mycobacterium seemed to be acquired at 49 and 119 DAH, respectively. Significant co-occurrence was found between Tenacibaculum and both Photobacterium and Vibrio. On the other hand, significantly negative correlations were detected between Vibrio and Streptococcus, Bacillus, Limosilactobacillus and Gardnerella. Our work reinforces the importance of life cycle studies, which can contribute to improve production husbandry strategies. However, we still need more information on this topic as repetition of patterns in different settings is essential to confirm our findings.
RESUMO
Aquaculture is a fundamental sector of the food industry nowadays. However, to become a sustainable and more profitable industry, it is necessary to monitor several associated parameters, such as temperature, salinity, ammonia, potential of hydrogen, nitrogen dioxide, bromine, among others. Their regular and simultaneous monitoring is expected to predict and avoid catastrophes, such as abnormal fish mortality rates. In this paper, we propose a novel anomaly detection approach for the early prediction of high fish mortality based on a multivariate Gaussian probability model. The goal of this approach is to determine the correlation between the number of daily registered physicochemical parameters of the fish tank water and the fish mortality. The proposed machine learning model was fitted with data from the weaning and pre-fattening phases of Senegalese sole (Solea senegalensis) collected over 2018, 2019, and 2020. This approach is suitable for real-time tracking and successful prediction of up to 80% of the high fish mortality rates. To the best of our knowledge, the proposed anomaly detection approach is the first time studied and applied in the framework of the aquaculture industry.